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Governance

Your organization writes the policy. And your workforce works inside it.

OffAskAuto

Policy result: Ask. The work returns with the decision and context attached.

Action handling, model bounds, spend ceilings, what the workforce is allowed to learn, and the record of every change are all set once at the organization and inherited by every project. Autonomy is the baseline, and the consequences stay yours to decide.

Enterprise Scale

AI software engineering at enterprise scale runs on orchestration, security, and governance.

At enterprise scale, the work stops being one engineer coaxing a model in a chat window and becomes a managed workforce. Lead Dev orchestrates the specialists, security controls remain active, and governance keeps action outcomes and review paths visible.

Say Yes, Safely

The velocity your devs want. The control your CISO needs.

Adopt autonomous AI without betting the company on it. Your organization sets action policy, branch policy decides where work can land, and model bounds decide which approved routes the workforce can use. When covered work needs judgment, it returns the call to your team.

The Non-Negotiables

Hard limits stay hard

Protected branches, production credentials, and spend limits remain separate from specialist capability.

Access scoped to the work

Connected repositories, approved context, Secrets, and temporary credential exposure follow their own project and organization controls.

Model bounds owned centrally

Your organization sets the model bounds: approved models, provider rules, and spend policy. Profiles choose how work runs inside them.

Governed actions stay on the record

Recorded approvals, checks, stops, and governed actions stay attached to the work they touched.

Autonomy is the baseline.

Every specialist can carry work autonomously. Your organization controls the consequences through action policy, protected resources, review paths, and hard safety rails.

Most work needs review

Supervised

Lead Dev reviews most changes before they land.

What happens
  • Changes come back to Lead Dev before they land
  • Best fit for new specialists, sensitive code, or unfamiliar repos
Routine work can continue

Trusted

Routine work can proceed; risky actions return to Lead Dev.

What happens
  • Scoped changes move with standard checks
  • Boundary crossings still return to Lead Dev
Broad work can continue

Autonomous

Most work proceeds on its own; risky actions still return to Lead Dev.

What happens
  • Broad work continues without per-item review
  • Lead Dev still catches boundary crossings and safety stops
Spend

Spending is governed like every other action.

The organization sets what may be spent, a project can hold something lower, and a single specialist can be narrower still. Work that reaches a ceiling follows the review path an admin chose rather than stopping quietly.

Explore Spend Controls
Budget

Every ceiling nests inside the one above it

Production Workflow

How production-grade software gets made at AI scale.

Start with a conversation that goes far beyond architecture and into business needs, too

Talk through the customer need, business pressure, product feel, and technical path. Lead Dev turns the conversation into a spec your team can steer before code starts.

Adversarial reviews are part of the everyday workflow

Lead Dev checks assumptions, edge cases, standards, and high-stakes calls. When a decision needs another check, click and request a Second Opinion with quick select for any model anytime.

Security and quality run on deterministic controls

Work comes back checked against your Quality Gates and deterministic security checks, including deep provenance and reasoning behind decisions that show how and why work was governed by Lead Dev.

Specialists use the product before your customers do

Specialists use the product by simulating real users in the browser, find broken flows, and learn from what your team accepts, corrects, or sends back.

The future of agent work belongs to managed workforces.